Publications

Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. A recurrent deep neural network was developed that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. The model was trained and evaluated using data from a multi-hospital health system between 2012 and 2019, with approximately 2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. The model enables safe avoidance of overnight monitoring for approximately 50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable.